{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['+', '-', ' ', 'P'],\n",
       "       [' ', 'W', ' ', ' '],\n",
       "       [' ', ' ', ' ', ' '],\n",
       "       [' ', ' ', ' ', ' ']], dtype='<U2')"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from Gridworld import Gridworld\n",
    "\n",
    "env = Gridworld()\n",
    "env.display()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# env.makeMove('d')\n",
    "# print(env.display())\n",
    "# env.reward()\n",
    "# env.board.render_np()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training...:   0%|          | 0/1000 [00:00<?, ?it/s]/home/fd_chen/anaconda3/envs/procthor-rl/lib/python3.9/site-packages/torch/nn/modules/loss.py:536: UserWarning: Using a target size (torch.Size([1])) that is different to the input size (torch.Size([])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n",
      "  return F.mse_loss(input, target, reduction=self.reduction)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training...: 100%|██████████| 1000/1000 [00:28<00:00, 35.58it/s]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import torch\n",
    "from tqdm import tqdm\n",
    "\n",
    "model = torch.nn.Sequential(\n",
    "    torch.nn.Linear(64, 150),\n",
    "    torch.nn.ReLU(),\n",
    "    torch.nn.Linear(150, 100),\n",
    "    torch.nn.ReLU(),\n",
    "    torch.nn.Linear(100, 4),\n",
    ")\n",
    "action_set = {\n",
    "    0:'u',\n",
    "    1:'d',\n",
    "    2:'l',\n",
    "    3:'r'\n",
    "}\n",
    "loss_fn = torch.nn.MSELoss()\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=0.001)\n",
    "\n",
    "model.train()\n",
    "\n",
    "gamma = 0.9\n",
    "epsilon = 0.1\n",
    "epochs = 1000\n",
    "losses = []\n",
    "for epoch in tqdm(range(epochs),desc='Training...'):\n",
    "\n",
    "    game = Gridworld(size=4,mode='static')\n",
    "    state_ = game.board.render_np().reshape(1,64)+np.random.rand(1,64)/10.0\n",
    "    state1 = torch.from_numpy(state_).float()\n",
    "    status = 1\n",
    "\n",
    "    while status :\n",
    "        qval = model(state1) # 获取当前状态下的Q值\n",
    "        qval_ = qval.data.numpy()\n",
    "\n",
    "        if (np.random.random() < epsilon):\n",
    "            action_ = np.random.randint(0,4)\n",
    "        else:\n",
    "            action_ = np.argmax(qval_)\n",
    "\n",
    "        action = action_set[action_]\n",
    "        game.makeMove(action) # 执行动作\n",
    "        # 两步奖励\n",
    "        state2 = game.board.render_np().reshape(1,64)+np.random.rand(1,64)/10.0 # 获取下一状态\n",
    "        state2_ =torch.from_numpy(state2).float()\n",
    "        reward = game.reward() # 获取奖励\n",
    "\n",
    "        with torch.no_grad():\n",
    "            newQ = model(state2_) # 计算未来奖励 实现Q-learning ，未来奖励的计算方法\n",
    "        maxQ = torch.max(newQ)\n",
    "\n",
    "        if reward == -1: # 游戏还没有结束 更新\n",
    "            Y = reward +gamma*maxQ # 更新方法\n",
    "        else:\n",
    "            Y = reward  # 预测结果\n",
    "\n",
    "        Y = torch.tensor([Y]).detach().float()\n",
    "        X = qval.squeeze()[action_].float()\n",
    "\n",
    "        loss = loss_fn(X,Y)\n",
    "\n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        losses.append(loss.item())\n",
    "        optimizer.step()\n",
    "\n",
    "        state1 = state2_\n",
    "\n",
    "        if reward != -1:\n",
    "            status = 0 # 游戏结束\n",
    "\n",
    "        if epsilon > 0.1:\n",
    "            epsilon -= (1/epochs)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.plot(losses)\n",
    "print(losses)\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "def test_model(model,mode = 'static'):\n",
    "    i = 0\n",
    "    test_game = Gridworld(mode=mode)\n",
    "    state_ = test_game.board.render_np().reshape(1,64)+np.random.rand(1,64)/10.0\n",
    "    state = torch.from_numpy(state_).float()\n",
    "\n",
    "    if True:\n",
    "        print(\"initial state:\")\n",
    "        print(test_game.display())\n",
    "\n",
    "    status = 1\n",
    "    while status == 1:\n",
    "        qval = model(state)\n",
    "        qval_ = qval.data.numpy()\n",
    "\n",
    "        action_ = np.argmax(qval_)\n",
    "        action = action_set[action_]\n",
    "        if True:\n",
    "            print(f\"{i}action :\",action)\n",
    "        test_game.makeMove(action)\n",
    "\n",
    "        state_ = test_game.board.render_np().reshape(1,64)+np.random.rand(1,64)/10.0\n",
    "        state = torch.from_numpy(state_).float()\n",
    "\n",
    "        if True:\n",
    "            print(test_game.display())\n",
    "        \n",
    "        reward = test_game.reward()\n",
    "\n",
    "        if reward != -1:\n",
    "            if reward >0:\n",
    "                status = 2\n",
    "                print(f\"game won ! reward : {reward}\")\n",
    "            else:\n",
    "                status =0\n",
    "                print(f\"game lost reward:{reward}\")\n",
    "        i +=1\n",
    "        if (i>15):\n",
    "            print(\"lost\")\n",
    "            break;\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "initial state:\n",
      "[['+' '-' ' ' 'P']\n",
      " [' ' 'W' ' ' ' ']\n",
      " [' ' ' ' ' ' ' ']\n",
      " [' ' ' ' ' ' ' ']]\n",
      "0action : d\n",
      "[['+' '-' ' ' ' ']\n",
      " [' ' 'W' ' ' 'P']\n",
      " [' ' ' ' ' ' ' ']\n",
      " [' ' ' ' ' ' ' ']]\n",
      "1action : d\n",
      "[['+' '-' ' ' ' ']\n",
      " [' ' 'W' ' ' ' ']\n",
      " [' ' ' ' ' ' 'P']\n",
      " [' ' ' ' ' ' ' ']]\n",
      "2action : l\n",
      "[['+' '-' ' ' ' ']\n",
      " [' ' 'W' ' ' ' ']\n",
      " [' ' ' ' 'P' ' ']\n",
      " [' ' ' ' ' ' ' ']]\n",
      "3action : l\n",
      "[['+' '-' ' ' ' ']\n",
      " [' ' 'W' ' ' ' ']\n",
      " [' ' 'P' ' ' ' ']\n",
      " [' ' ' ' ' ' ' ']]\n",
      "4action : l\n",
      "[['+' '-' ' ' ' ']\n",
      " [' ' 'W' ' ' ' ']\n",
      " ['P' ' ' ' ' ' ']\n",
      " [' ' ' ' ' ' ' ']]\n",
      "5action : u\n",
      "[['+' '-' ' ' ' ']\n",
      " ['P' 'W' ' ' ' ']\n",
      " [' ' ' ' ' ' ' ']\n",
      " [' ' ' ' ' ' ' ']]\n",
      "6action : u\n",
      "[['+' '-' ' ' ' ']\n",
      " [' ' 'W' ' ' ' ']\n",
      " [' ' ' ' ' ' ' ']\n",
      " [' ' ' ' ' ' ' ']]\n",
      "game won ! reward : 10\n"
     ]
    }
   ],
   "source": [
    "test_model(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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